Building a WIM Data Archive for Improved Modeling, Design, and Rating

1. Building a WIM Data Archive for Improved Modeling, Design, and Rating Christopher Monsere Assistant Professor, Portland State University Andrew Nichols Assistant Professor, Marshall University NATMEC 2008, Washington, D.C. August 6, 2008

2. Outline • Data Almanac • PORTAL • WIM Archive Quality Control • Sample Uses of the WIM Archive • Sensor Health and Calibration • Uses for LFR and MPDEG • System Performance and Information • Planning Data

3. Describe the data source DATA almanac

4. Data Almanac • 22 reporting WIM sites • All upstream of weigh stations • All are CVISN sites • April 2005 - March 2008 • 2007 - 12,054,552 trucks • Intermittent data outages and problems • Data quality and accuracy?

5. Data Almanac • These WIM sites provide • Axle weights • Gross vehicle weight • Spacing • Vehicle class • Unique transponder numbers

7. Axle Weight Sensors • Single load cells • Sensors weigh vehicles traveling at normal highway speeds • Weight measurement affected by many factors • Site characteristics • Environmental factors • Truck dynamics

8. WIM Layout Trucks Bypass R.F. Antenna 1 mile Notification Station (in-cab) High Speed Tracking System WIM Sorting Site Ramp (with Overhead AVI) Static Sorter Weighing System

9. Sorting Lanes Exit Lane Weigh Lane • WIM installed on entrance ramp to determine if vehicle is close to legal weight • Legal trucks directed to exit facility without being weighed statically

10. Indiana WIM Site Layout • Loops – vehicle presence & speed measurement • Load Cell – weight & speed • Piezo – speed & axle spacing Inductive Loop Piezo Sensor Load Cell Sensor Inductive Loop

11. WIM Classification Algorithm • Portion related to 5-axle vehicles shown • Works like a sieve • Min/Max thresholds for • # of axles • axle spacing • axle weight • gvw • Currently only use axle spacing

12. PrePass NORPASS State operated/developed; compatible with NORPASS Electronic screening J. Lane, Briefing to American Association of State Highway and Transportation Officials (AASHTO), 22 February 2008 freight.transportation.org/doc/hwy/dc08/scoht_cvisn.ppt • Three types of tags • Heavy Vehicle Electronic License Plate (HELP)’s PrePass program • North American Pre-clearance and Safety System (NORPASS) • Oregon Green Light Program • All RF tags

13. What is PORTAL PORTAL - Postgresql database PORTAL 101

14. Describe Quality Control Metrics QUALITY CONTROL

15. Outline • Summarize Existing Quality Control Metrics • Speed Accuracy • Weight Accuracy • System-Wide Monitoring vs. Site-based Monitoring • SQL ServerExtract Pivot Tables (.CUB) • Move Beyond Heuristic (sometimes visual) Rules to a Statistically Solid Decision Making Criteria • Learn from Manufacturing World of Statistical Process Control

16. Data Quality Assessment • Obtain data from WIM sites (nightly or weekly) • Upload individual records to SQL database • Macroscopic view to identify potential problems • Microscopic view to investigate causes • Differentiate between problems & random noise using statistical process control (individual lanes) • Schedule maintenance/calibration accordingly

17. Past Quality Control Hurdles • Emerging WIM quality control metrics • Enormous amounts of data • Many vendors’ standard reports do not provide level of detail necessary for effective quality control, particularly comparing multiple sites • Most DOTs do not have time or resources to perform QA/QC

18. Accuracy Metrics Illustrated • Vehicle Classification • Axle Spacing • Speed • Axle Weight • Wheel weight Accuracy Metric Accuracy Metric

19. Speed Accuracy Metric • Class 9 drive tandem axle spacing • Currently used by many agencies • Target values based on manufacturer sales data • Individual trucks 4.25-4.58 feet • Population average 4.30-4.36 feet

20. 2002 Sales Data • Data obtained from 3 of top 6 truck manufacturers • Verified at Indiana WIM sites with accurate speed calibration Weighted Average = 4.33 feet Modal Value (0.01)’ would be even better than average

21. WIM Speed Control Chart • Class 9 Drive Tandem Axle Spacing • Limits based on sales data and data from sites with good calibration • Limits same for all sites • Upper Control Limit (UCL) = 4.36 feet • Centerline (CL) = 4.33 feet • Lower Control Limit (LCL) = 4.30 feet

22. Speed & Axle Spacing • WIM calculates speed by measuring vehicle time between two sensors • WIM speed used to calculate axle spacing based on time between consecutive axles Calibration factor

23. Speed Accuracy Importance • Used to calculate axle spacing • Vehicle classification • Bridge formula compliance for enforcement • Used to apply weight calibration factors in some WIM systems

24. Why Unclassified? • All axle spacings in this lane were overestimated by ~15% • The axle spacing 3-4 is exceeding the defined thresholds in the vehicle classification file (40’ max) Axle 3-4 Spacing 30-35 feet typical

25. Weight Accuracy Metric • Class 9 front axle weight • Individual trucks 8,000-12,000 lbs • Population average 9,000-11,000 lbs • Currently used by many agencies • This range is too big for detecting subtle drifts

26. Class 9 WIM Accuracy Metrics • Steer Axle Weight • 8.5 kips (GVW < 32 kips) • 9.3 kips (GVW 32-70 kips) • 10.4 kips (GVW > 70 kips) • Steer Axle Weight and Axle 1-2 Spacing • Logarithmic relationship • Steer Axle Weight/Axle 1-2 Spacing • Steer Axle Wheel Weight • Gross Vehicle Weight Distribution • 28-32 kips unloaded • 70-80 kips loaded

27. Dahlin’s GVW Criteria 3rd Peak?

28. Class 9 GVW Distribution • Modal distributions • Look for peak mode shifts • Trend identification difficult • Very difficult to automate (QC) • Step function to find max GVW bins • Bin width affects precision

29. Mixture Models • Fitting a continuous function to GVW distribution – combination of normal distributions • Use algorithm to fit “mixture” of normal distributions • Obtain mean and covariance for each component

30. GVW Fit – 1 Component Mean 1 = 56 kips Covariance 1 = 310 kips Proportion 1 = 100%

31. GVW Fit – 2 Components Mean 1 = 74 kips Covariance 1 = 11 kips Mean 2 = 48 kips Covariance 2 = 222 kips Proportion 1 = 33% Proportion 2 = 67%

32. GVW Fit – 3 Components Mean 1 = 75 kips Covariance 1 = 9 kips Mean 2 = 52 kips Covariance 2 = 193 kips Mean 3 = 30 kips Covariance 3 = 11 kips Proportion 1 = 31% Proportion 2 = 57% Proportion 3 = 12%

33. WIM Speed/Axle Spacing Accuracy • Common speed accuracy metric isClass 9 drive tandem axle spacing • No consensus on proper values • Verified with manufacturer sales numbers • Individual trucks 4.25-4.58 feet • Population average 4.30-4.36 feet

34. Describe from Quality Control Metrics About Sensor Health SENSOR HEALTH

38. Describe Higgins Work LFR and MPDEG

39. Describe Sample PMs System performance

40. Freight performance metrics • Average travel times on key corridors • Ton-miles on each corridor by various temporal considerations • Overweight vehicles on corridors by temporal variation (measuring change) • Seasonal variability in loading, routes, and volumes • Percent trucks with tags on each corridor • Origin-destination matrix

41. Freight performance metrics

42. Freight performance metrics Using Federal Highway Administration (FHWA) / American Transportation Research Institute (ATRI) proprietary truck satellite data.

43. Wyeth Emigrant Hill Farewell Bend

48. Next Steps

49. Acknowledgements Dave McKane and Dave Fifer, ODOT Kristin Tufte and Heba Alawakiel, PSU